隐藏不实信息比突出显示更易赢得用户信任。
Hide or Highlight: Understanding the Impact of Factuality Expression on User Trust
- 用模糊或移除方式隐藏低可信内容
- 两种隐藏策略信任度更高，质量感知不变
- 适合需提升用户信赖的AI交互场景
大型语言模型常生成看似合理但事实错误的内容。为防止用户盲目信任AI导致误判，研究者尝试通过不同方式向用户披露事实性评估。然而，公开被判定为不准确的内容，是否比完全隐藏更影响用户信任尚不明确。我们测试了四种披露策略：透明（突出低可信内容）、注意力（突出高可信内容）、不透明（移除低可信内容）、模糊（使低可信内容模糊），并与无事实信息的基线对比。在148名受试者的问答任务中发现，不透明和模糊策略显著提升了用户信任，同时保持了对答案质量的正面感知。结果表明，隐藏可能不实内容是建立用户信任的有效方式。
原文摘要 · Abstract (English)
Large language models are known to produce outputs that are plausible but factually incorrect. To prevent people from making erroneous decisions by blindly trusting AI, researchers have explored various ways of communicating factuality estimates in AI-generated outputs to end-users. However, little is known about whether revealing content estimated to be factually incorrect influences users' trust when compared to hiding it altogether. We tested four different ways of disclosing an AI-generated output with factuality assessments: transparent (highlights less factual content), attention (highlights factual content), opaque (removes less factual content), ambiguity (makes less factual content vague), and compared them with a baseline response without factuality information. We conducted a human subjects research (N = 148) using the strategies in question-answering scenarios. We found that the opaque and ambiguity strategies led to higher trust while maintaining perceived answer quality, compared to the other strategies. We discuss the efficacy of hiding presumably less factual content to build end-user trust.
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